Probing star formation in the dense environments of z ∼ 1 lensing haloes aligned with dusty star-forming galaxies detected with the South Pole Telescope
Bibliographic record
Abstract
We probe star formation in the environments of massive (∼1013 M⊙) dark matter haloes at redshifts of z ∼ 1. This star formation is linked to a submillimetre clustering signal which we detect in maps of the Planck High Frequency Instrument that are stacked at the positions of a sample of high redshift (z > 2) strongly lensed dusty star-forming galaxies (DSFGs) selected from the South Pole Telescope (SPT) 2500 deg2 survey. The clustering signal has submillimetre colours which are consistent with the mean redshift of the foreground lensing haloes (z ∼ 1). We report a mean excess of star formation rate (SFR) compared to the field, of (2700 ± 700) M⊙ yr−1 from all galaxies contributing to this clustering signal within a radius of 3.5 arcmin from the SPT DSFGs. The magnitude of the Planck excess is in broad agreement with predictions of a current model of the cosmic infrared background. The model predicts that 80 per cent of the excess emission measured by Planck originates from galaxies lying in the neighbouring haloes of the lensing halo. Using Herschel maps of the same fields, we find a clear excess, relative to the field, of individual sources which contribute to the Planck excess. The mean excess SFR compared to the field is measured to be (370 ± 40) M⊙ yr−1 per resolved, clustered source. Our findings suggest that the environments around these massive z ∼ 1 lensing haloes host intense star formation out to about 2 Mpc. The flux enhancement due to clustering should also be considered when measuring flux densities of galaxies in Planck data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".